Multi-modal deep learning networks for RGB-D pavement waste detection and recognition.

To create a clean living environment, governments around the world have hired a large number of workers to clean up waste on pavements, which is inefficient for waste management. To better alleviate this problem, relevant scholars have proposed several deep learning methods based on RGB images to achieve waste detection and recognition. Considering the limitations of color images, we propose an efficient multi-modal learning solution for pavement waste detection and recognition. Specifically, we construct a high-quality outdoor pavement waste dataset called OPWaste, which is more in line with real needs. Compared to other waste datasets, OPWaste dataset not only has the advantages of rich background and high diversity, but also provides color and depth images. Meanwhile, we explore six different multi-modal fusion methods and propose a novel multi-modal multi-scale network (MM-Net) for RGB-D waste detection and recognition. MM-Net introduces a novel multi-scale refinement module (MRM) and multi-scale interaction module (MIM). MRM can effectively refine critical features using attention mechanisms. MIM can gradually realize information interaction between hierarchical features. In addition, we select several representative methods and perform comparative experiments. Experimental results show that MM-Net based on the image addition fusion method outperforms other deep learning models and reaches 97.3% and 84.4% on mAP0.5 and AR metrics. In fact, multi-modal learning plays an important role in intelligent waste recycling. As a promising auxiliary tool, our solution can be applied to intelligent cleaning robots for automatic outdoor waste management.

SLC7A11, a potential immunotherapeutic target in lung adenocarcinoma.

SLC7A11 has significant translational value in cancer treatment. However, there are few studies on whether SLC7A11 affects the immune status of lung adenocarcinoma (LUAD). Information on SLC7A11 expression and its impact on prognosis was obtained from the cancer genome atlas and gene expression omnibus databases. The differentially expressed genes (DEGs) were analysed by GO and KEGG. GSEA enrichment analysis was performed in the SLC7A11-high and SLC7A11-low groups. The relationship between SLC7A11 and tumour immunity, immune checkpoints, and immune cell infiltration was studied using R language. We analysed the correlation between SLC7A11 and chemotactic factors (CFs) and chemokine receptors using the TISIDB database. SLC7A11 is overexpressed in many tumours, including LUAD. The 5-year overall survival of patients in the SLC7A11-high group was lower than in the SLC7A11-low group. KEGG analysis found that the DEGs were enriched in ferroptosis signaling pathways. GSEA analysis found that the survival-related signaling pathways were enriched in the SLC7A11-low group. The SLC7A11-low group had higher immune scores and immune checkpoint expression. SLC7A11 was negatively correlated with many immune cells (CD8+ T cells, immature dendritic cells), CFs, chemokine receptors (such as CCL17/19/22/23, CXCL9/10/11/14, CCR4/6, CX3CR1, CXCR3) and MHCs (major histocompatibility complex). SLC7A11 may regulate tumour immunity and could be a potential therapeutic target for LUAD.

Nitrogen removal efficiency of surface flow constructed wetland for treating slightly polluted river water.

Restoration and water quality improvement of malodorous as well as slightly polluted rivers have been the global focus for environmental protection research and the development and construction of sponge cities. To date, constructed wetlands have been proven to be one of efficient methods to improve water quality. Nitrogen removal efficiency is a crucial indicator for the performance evaluation in slightly polluted river water treatment. Therefore, current study aimed to investigate the N removal efficiency of 3-stage surface flow constructed wetlands for water treatment. Results show that after a prolonged operation period, constructed wetlands were able to remove NH4+-N, NO3--N, and TN by 38.4%, 22.3%, and 29.1%, respectively. Further investigations were carried out to investigate the removal efficiency of various N species in the 3-stage wetlands. Findings reveal that NH4+-N was mainly treated in wetland #1 (W1) and wetland #2 (W2), while NO3--N and TN were in wetland #2 (W2) and wetland #3 (W3). Results also reveal that the influencing factors such as hydraulic retention time (HRT), water temperature (WT), and additional carbon source have significant effect on the removal performance of constructed wetlands.